Analyze Feedback using AWS Comprehend
Instantly convert unstructured student feedback into actionable insights.
Trusted by
Built on AWS’s enterprise-grade NLP service (Amazon Comprehend) and proven in education-analytics contexts.
Success Story
Similar AI tools helped the University of Thessaly process 700+ student comments in one study, enabling detection of teaching-competency issues across courses.
Integrates with
Problem
Many higher-education institutions collect large volumes of open-ended student survey feedback (e.g., “What did you like / dislike about the course?”). But manually reviewing each comment is slow, inconsistent and rarely yields actionable insights in time. As a result, teaching teams miss early signals of student issues, courses don’t evolve quickly, and student satisfaction and learning outcomes suffer.
Solution
This agent automates the processing of all textual feedback using AWS Comprehend. It extracts sentiment (positive/neutral/negative), identifies recurring themes and key phrases, and flags high-priority issues (e.g., “lectures too fast”, “unclear assignments”). Results are posted into a Mattermost channel and/or visual dashboard, enabling academic teams to coordinate responses immediately.
Result
Users can expect insights within minutes rather than days, earlier detection of issues, improved teacher responsiveness, and stronger student satisfaction/trust in evaluation processes.
Use Cases
The “Analyze Feedback using AWS Comprehend” agent helps educational institutions turn mountains of open-ended student comments into meaningful, actionable insights — in minutes instead of days. Leveraging Amazon Comprehend for sentiment detection and key-phrase extraction, it automatically ingests feedback-survey responses (via CSV/JSON or live form), analyses sentiment and themes, and posts structured dashboards and alerts to Mattermost channels for academic leadership and teaching teams. By surfacing negative or neutral sentiments quickly, the agent enables proactive teaching improvement, faster course corrections, and improved student satisfaction — all without manual review of hundreds of text responses.
Integrations
Connect to your existing tools seamlessly
Technology Stack
Automation
Automation
Infrastructure
Implementation Timeline
Setup & Data Mapping
2 daysDefine survey sources, feedback fields, tagging schema
Pipeline Build
4 daysBuild AWS workflow: ingest, analyse, store results
Integration & Alerts
2 daysConfigure Mattermost channel, alert logic, dashboards
Pilot & Validation
1 weekRun on prior semester’s comments, refine tags/thresholds
Live Deployment
4 daysSwitch to live feedback ingestion, train teaching teams
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